A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet

Abstract

Along with their physical health, modern people also need to manage the health of their scalp and hair due to changes in lifestyle habits, job stress, and environmental pollution. In this study, a machine learning model was developed to diagnose scalp conditions such as fine dandruff and perifollicular erythema. Then, transfer learning was conducted using EfficientNet-B0. A web platform that allows users to easily diagnose the condition of their scalp was also proposed. The results showed that the accuracy of the diagnosis model for fine dandruff and perifollicular erythema was 75% and 82%, respectively. It showed good performance in classifying normal, mild, moderate, and severe cases compared to previous studies. Finally, a fast and convenient web platform was developed where users can upload an image and immediately visualize their scalp condition, receive diagnostic results, and see similar cases and solutions. The analysis of user satisfaction indicates that this web application has achieved exceptional outcomes in terms of user satisfaction, garnering high evaluations for its usability, design effectiveness, and overall user experience. This setup enables users to easily check their scalp condition and is accessible to everyone, which is a significant advantage. This is expected to play a crucial role in contributing to global scalp health by advocating the benefits of the early detection and treatment of scalp-related conditions.

Keywords

• machine learning
• EfficientNet
• scalp diagnosis
• fine dandruff
• perifollicular erythema
• web platform